Point-of-care SERS platforms: integrating microfluidics and machine learning for disease screening

Biqing Chen; Xiaohong Qiu; Yang Li

doi:10.1039/d6lc00202a

January 1, 2026Lab on a Chip

Point-of-care SERS platforms: integrating microfluidics and machine learning for disease screening

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Key Points

The central aim is to explore the effectiveness of integrating microfluidics and machine learning in SERS platforms for disease screening.
Utilized microfluidic SERS platforms for bioanalysis
Integrated plasmonic elements for multiplexed biomarker detection
Applied machine learning algorithms for data analysis
Achieved ultrasensitive detection of biomarkers
Enabled high-throughput analysis for faster diagnostics
Demonstrated potential for clinical translation with improved bioanalytical performance

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Implication

Study integrates microfluidics and machine learning to enhance disease screening, suggesting improved detection methods.

Abstract

Microfluidic SERS platforms enable ultrasensitive, high-throughput bioanalysis via Raman enhancement and microfluidic control. Plasmonic integration supports multiplexed biomarker detection for biomedical research and clinical translation.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a056695a550a87e60a1e84f https://doi.org/10.1039/d6lc00202a

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Point-of-care SERS platforms: integrating microfluidics and machine learning for disease screening

Key Points

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Implication

Abstract

Discussion

Cite This Study

Point-of-care SERS platforms: integrating microfluidics and machine learning for disease screening

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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